OpenRaiser/Intern-Atlas huggingface dataset:
IfcWoD, Semantically Adapting IFC Model Relations into OWL Properties
Venue: arXiv cs.AI 2015
Year: 2015

In the context of Building Information Modelling, ontologies have been identified as interesting in achieving information interoperability. Regarding the construction and facility management domains, several IFC (Industry Foundation Classes) based ontologies have been developed, such as IfcOWL. In the context of ontology modelling, the constraint of optimizing the size of IFC STEP-based files can be leveraged. In this paper, we propose an adaptation of the IFC model into OWL which leverages from all modelling constraints required by the object-oriented structure of IFC schema. Therefore, we do not only present a syntactic but also a semantic adaptation of the IFC model. Our model takes into consideration the meaning of entities, relationships, properties and attributes defined by the IFC standard. Our approach presents several advantages compared to other initiatives such as the optimization of query execution time. Every advantage is defended by means of practical examples and benchmarks.
Prediction of the Yield of Enzymatic Synthesis of Betulinic Acid Ester Using Artificial Neural Networks and Support Vector Machine
Venue: arXiv cs.LG 2015
Year: 2015

3\b{eta}-O-phthalic ester of betulinic acid is of great importance in anticancer studies. However, the optimization of its reaction conditions requires a large number of experimental works. To simplify the number of times of optimization in experimental works, here, we use artificial neural network (ANN) and support vector machine (SVM) models for the prediction of yields of 3\b{eta}-O-phthalic ester of betulinic acid synthesized by betulinic acid and phthalic anhydride using lipase as biocatalyst. General regression neural network (GRNN), multilayer feed-forward neural network (MLFN) and the SVM models were trained based on experimental data. Four indicators were set as independent variables, including time (h), temperature (C), amount of enzyme (mg) and molar ratio, while the yield of the 3\b{eta}-O-phthalic ester of betulinic acid was set as the dependent variable. Results show that the GRNN and SVM models have the best prediction results during the testing process, with comparatively low RMS errors (4.01 and 4.23respectively) and short training times (both 1s). The prediction accuracy of the GRNN and SVM are both 100% in testing process, under the tolerance of 30%.
Smart Transmission Network Emergency Control
Venue: arXiv cs.SY 2015
Year: 2015

Power systems normally operate at their stable operating conditions where the power supply and demand are balanced. In emergency situations, the operators proceed to cut a suitable amount of loads to rebalance the supply-demand and hopefully stabilize the system. This traditional emergency control scheme results in interrupted service with severely economic damages to customers. In order to provide seamless electricity service to customers, this paper proposes a viable alternative for traditional remedial controls of power grids by exploiting the plentiful transmission facilities. In particular, we consider two emergency control schemes involving adjustment of the susceptance of a number of selected transmission lines to drive either fault-on dynamics or post-fault dynamics, and thereby stabilize the system under emergency situations. The corresponding emergency control problems will be formulated and partly solved in some specific cases. Simple numerical simulation will be used to illustrate the concept of this paper.
Multimodal Skip-gram Using Convolutional Pseudowords
Venue: arXiv cs.CL 2015
Year: 2015

This work studies the representational mapping across multimodal data such that given a piece of the raw data in one modality the corresponding semantic description in terms of the raw data in another modality is immediately obtained. Such a representational mapping can be found in a wide spectrum of real-world applications including image/video retrieval, object recognition, action/behavior recognition, and event understanding and prediction. To that end, we introduce a simplified training objective for learning multimodal embeddings using the skip-gram architecture by introducing convolutional "pseudowords:" embeddings composed of the additive combination of distributed word representations and image features from convolutional neural networks projected into the multimodal space. We present extensive results of the representational properties of these embeddings on various word similarity benchmarks to show the promise of this approach.
Newtonian Image Understanding: Unfolding the Dynamics of Objects in Static Images
Venue: arXiv cs.CV 2015
Year: 2015

In this paper, we study the challenging problem of predicting the dynamics of objects in static images. Given a query object in an image, our goal is to provide a physical understanding of the object in terms of the forces acting upon it and its long term motion as response to those forces. Direct and explicit estimation of the forces and the motion of objects from a single image is extremely challenging. We define intermediate physical abstractions called Newtonian scenarios and introduce Newtonian Neural Network ($N^3$) that learns to map a single image to a state in a Newtonian scenario. Our experimental evaluations show that our method can reliably predict dynamics of a query object from a single image. In addition, our approach can provide physical reasoning that supports the predicted dynamics in terms of velocity and force vectors. To spur research in this direction we compiled Visual Newtonian Dynamics (VIND) dataset that includes 6806 videos aligned with Newtonian scenarios represented using game engines, and 4516 still images with their ground truth dynamics.
Expressiveness and Understandability Considerations of Hierarchy in Declarative Business Process Models
Venue: arXiv cs.SE 2015
Year: 2015

Hierarchy has widely been recognized as a viable approach to deal with the complexity of conceptual models. For instance, in declarative business process models, hierarchy is realized by sub-processes. While technical implementations of declarative sub-processes exist, their application, semantics, and the resulting impact on understandability are less understood yet-this research gap is addressed in this work. In particular, we discuss the semantics and the application of hierarchy and show how sub-processes enhance the expressiveness of declarative modeling languages. Then, we turn to the impact on the understandability of hierarchy on a declarative process model. To systematically assess this impact, we present a cognitive-psychology based framework that allows to assess the possible impact of hierarchy on the understandability of the process model.
Software Analytics to Software Domains: A Systematic Literature Review
Venue: arXiv cs.SE 2015
Year: 2015

Software Analytics (SA) is a new branch of big data analytics that has recently emerged (2011). What distinguishes SA from direct software analysis is that it links data mined from many different software artifacts to obtain valuable insights. These insights are useful for the decision-making process throughout the different phases of the software lifecycle. Since SA is currently a hot and promising topic, we have conducted a systematic literature review, presented in this paper, to identify gaps in knowledge and open research areas in SA. Because many researchers are still confused about the true potential of SA, we had to filter out available research papers to obtain the most SA-relevant work for our review. This filtration yielded 19 studies out of 135. We have based our systematic review on four main factors: which software practitioners SA targets, which domains are covered by SA, which artifacts are extracted by SA, and whether these artifacts are linked or not. The results of our review have shown that much of the available SA research only serves the needs of developers. Also, much of the available research uses only one artifact which, in turn, means fewer links between artifacts and fewer insights. This shows that the available SA research work is still embryonic leaving plenty of room for future research in the SA field.
Interference Alignment-Aided Base Station Clustering using Coalition Formation
Venue: arXiv cs.IT 2015
Year: 2015

Base station clustering is necessary in large interference networks, where the channel state information (CSI) acquisition overhead otherwise would be overwhelming. In this paper, we propose a novel long-term throughput model for the clustered users which addresses the balance between interference mitigation capability and CSI acquisition overhead. The model only depends on statistical CSI, thus enabling long-term clustering. Based on notions from coalitional game theory, we propose a low-complexity distributed clustering method. The algorithm converges in a couple of iterations, and only requires limited communication between base stations. Numerical simulations show the viability of the proposed approach.
Whom Should We Sense in "Social Sensing" -- Analyzing Which Users Work Best for Social Media Now-Casting
Venue: arXiv cs.SI 2015
Year: 2015

Given the ever increasing amount of publicly available social media data, there is growing interest in using online data to study and quantify phenomena in the offline "real" world. As social media data can be obtained in near real-time and at low cost, it is often used for "now-casting" indices such as levels of flu activity or unemployment. The term "social sensing" is often used in this context to describe the idea that users act as "sensors", publicly reporting their health status or job losses. Sensor activity during a time period is then typically aggregated in a "one tweet, one vote" fashion by simply counting. At the same time, researchers readily admit that social media users are not a perfect representation of the actual population. Additionally, users differ in the amount of details of their personal lives that they reveal. Intuitively, it should be possible to improve now-casting by assigning different weights to different user groups. In this paper, we ask "How does social sensing actually work?" or, more precisely, "Whom should we sense--and whom not--for optimal results?". We investigate how different sampling strategies affect the performance of now-casting of two common offline indices: flu activity and unemployment rate. We show that now-casting can be improved by 1) applying user filtering techniques and 2) selecting users with complete profiles. We also find that, using the right type of user groups, now-casting performance does not degrade, even when drastically reducing the size of the dataset. More fundamentally, we describe which type of users contribute most to the accuracy by asking if "babblers are better". We conclude the paper by providing guidance on how to select better user groups for more accurate now-casting.
Seeing the Unseen Network: Inferring Hidden Social Ties from Respondent-Driven Sampling
Venue: arXiv cs.SI 2015
Year: 2015

Learning about the social structure of hidden and hard-to-reach populations --- such as drug users and sex workers --- is a major goal of epidemiological and public health research on risk behaviors and disease prevention. Respondent-driven sampling (RDS) is a peer-referral process widely used by many health organizations, where research subjects recruit other subjects from their social network. In such surveys, researchers observe who recruited whom, along with the time of recruitment and the total number of acquaintances (network degree) of respondents. However, due to privacy concerns, the identities of acquaintances are not disclosed. In this work, we show how to reconstruct the underlying network structure through which the subjects are recruited. We formulate the dynamics of RDS as a continuous-time diffusion process over the underlying graph and derive the likelihood for the recruitment time series under an arbitrary recruitment time distribution. We develop an efficient stochastic optimization algorithm called RENDER (REspoNdent-Driven nEtwork Reconstruction) that finds the network that best explains the collected data. We support our analytical results through an exhaustive set of experiments on both synthetic and real data.
Specifying a Realistic File System
Venue: arXiv cs.LO 2015
Year: 2015

We present the most interesting elements of the correctness specification of BilbyFs, a performant Linux flash file system. The BilbyFs specification supports asynchronous writes, a feature that has been overlooked by several file system verification projects, and has been used to verify the correctness of BilbyFs's fsync() C implementation. It makes use of nondeterminism to be concise and is shallowly-embedded in higher-order logic.
Modeling and Verification of the Bitcoin Protocol
Venue: arXiv cs.LO 2015
Year: 2015

Bitcoin is a popular digital currency for online payments, realized as a decentralized peer-to-peer electronic cash system. Bitcoin keeps a ledger of all transactions; the majority of the participants decides on the correct ledger. Since there is no trusted third party to guard against double spending, and inspired by its popularity, we would like to investigate the correctness of the Bitcoin protocol. Double spending is an important threat to electronic payment systems. Double spending would happen if one user could force a majority to believe that a ledger without his previous payment is the correct one. We are interested in the probability of success of such a double spending attack, which is linked to the computational power of the attacker. This paper examines the Bitcoin protocol and provides its formalization as an UPPAAL model. The model will be used to show how double spending can be done if the parties in the Bitcoin protocol behave maliciously, and with what probability double spending occurs.
A Survey on Reproducibility in Parallel Computing
Venue: arXiv cs.DC 2015
Year: 2015

We summarize the results of a survey on reproducibility in parallel computing, which was conducted during the Euro-Par conference in August 2015. The survey form was handed out to all participants of the conference and the workshops. The questionnaire, which specifically targeted the parallel computing community, contained questions in four different categories: general questions on reproducibility, the current state of reproducibility, the reproducibility of the participants' own papers, and questions about the participants' familiarity with tools, software, or open-source software licenses used for reproducible research.
On cyclotomic cosets and code constructions
Venue: arXiv cs.IT 2015
Year: 2015

New properties of $q$-ary cyclotomic cosets modulo $n = q^{m} - 1$, where $q \geq 3$ is a prime power, are investigated in this paper. Based on these properties, the dimension as well as bounds for the designed distance of some families of classical cyclic codes can be computed. As an application, new families of nonbinary Calderbank-Shor-Steane (CSS) quantum codes as well as new families of convolutional codes are constructed in this work. These new CSS codes have parameters better than the ones available in the literature. The convolutional codes constructed here have free distance greater than the ones available in the literature.
A Systematic Literature Review of the Critical Factors for Success of Mobile Learning in Higher Education (University Students' Perspective)
Venue: arXiv cs.CY 2015
Year: 2015

The phenomenon of the use of a mobile learning (m-Learning) platform in educational institutions is slowly gaining momentum. While this can be taken as an encouraging sign, the perplexing part is that the fervor with which mobile phones have been welcomed into every aspect of our lives does not seem to be evident in the educational sector. In order to understand the reason, it is important to understand user expectations of the system. This paper documents a systematic review of various research studies seeking to find the success factors for effective m-Learning. A total of 30 studies were included in the research, which combined would give a true picture of user perceptions of the factors they consider important for effective m-Learning implementation. Our systematic review collates results from 30 studies conducted in 17 countries, where 13 critical success factors (CSFs) were found to strongly impact m-Learning.
Client-Side Web Proxy Detection from Unprivileged Mobile Devices
Venue: arXiv cs.NI 2015
Year: 2015

Mobile devices that connect to the Internet via cellular networks are rapidly becoming the primary medium for accessing Web content. Cellular service providers (CSPs) commonly deploy Web proxies and other middleboxes for security, performance optimization and traffic engineering reasons. However, the prevalence and policies of these Web proxies are generally opaque to users and difficult to measure without privileged access to devices and servers. In this paper, we present a methodology to detect the presence of Web proxies without requiring access to low-level packet traces on a device, nor access to servers being contacted. We demonstrate the viability of this technique using controlled experiments, and present the results of running our approach on several production networks and popular Web sites. Next, we characterize the behaviors of these Web proxies, including caching, redirecting, and content rewriting. Our analysis can identify how Web proxies impact network performance, and inform policies for future deployments. Last, we release an Android app called Proxy Detector on the Google Play Store, allowing average users with unprivileged (non-rooted) devices to understand Web proxy deployments and contribute to our IRB-approved study. We report on results of using this app on 11 popular carriers from the US, Canada, Austria, and China.
A Framework for Planning and Controlling Non-Periodic Bipedal Locomotion
Venue: arXiv cs.RO 2015
Year: 2015

This study presents a theoretical framework for planning and controlling agile bipedal locomotion based on robustly tracking a set of non-periodic apex states. Based on the prismatic inverted pendulum model, we formulate a hybrid phase-space planning and control framework which includes the following key components: (1) a step transition solver that enables dynamically tracking non-periodic apex or keyframe states over various types of terrains, (2) a robust hybrid automaton to effectively formulate planning and control algorithms, (3) a phase-space metric to measure distance to the planned locomotion manifolds, and (4) a hybrid control method based on the previous distance metric to produce robust dynamic locomotion under external disturbances. Compared to other locomotion frameworks, we have a larger focus on non-periodic gait generation and robustness metrics to deal with disturbances. Such focus enables the proposed control framework to robustly track non-periodic apex states over various challenging terrains and under external disturbances as illustrated through several simulations. Additionally, it allows a bipedal robot to perform non-periodic bouncing maneuvers over disjointed terrains.
Using Text Mining To Analyze Real Estate Classifieds
Venue: arXiv cs.IR 2015
Year: 2015

Many brokers have adapted their operation to exploit the potential of the web. Despite the importance of the real estate classifieds, there has been little work in analyzing such data. In this paper we propose a two-stage regression model that exploits the textual data in real estate classifieds. We show how our model can be used to predict the price of a real estate classified. We also show how our model can be used to highlight keywords that affect the price positively or negatively. To assess our contributions, we analyze four real world data sets, which we gathered from three different property websites. The analysis shows that our model (which exploits textual features) achieves significantly lower root mean squared error across the different data sets and against variety of regression models.
Separation Surfaces in the Spectral TV Domain for Texture Decomposition
Venue: arXiv cs.CV 2015
Year: 2015

In this paper we introduce a novel notion of separation surfaces for image decomposition. A surface is embedded in the spectral total-variation (TV) three dimensional domain and encodes a spatially-varying separation scale. The method allows good separation of textures with gradually varying pattern-size, pattern-contrast or illumination. The recently proposed total variation spectral framework is used to decompose the image into a continuum of textural scales. A desired texture, within a scale range, is found by fitting a surface to the local maximal responses in the spectral domain. A band above and below the surface, referred to as the \textit{Texture Stratum}, defines for each pixel the adaptive scale-range of the texture. Based on the decomposition an application is proposed which can attenuate or enhance textures in the image in a very natural and visually convincing manner.
An Iterative Reweighted Method for Tucker Decomposition of Incomplete Multiway Tensors
Venue: arXiv cs.NA 2015
Year: 2015

We consider the problem of low-rank decomposition of incomplete multiway tensors. Since many real-world data lie on an intrinsically low dimensional subspace, tensor low-rank decomposition with missing entries has applications in many data analysis problems such as recommender systems and image inpainting. In this paper, we focus on Tucker decomposition which represents an Nth-order tensor in terms of N factor matrices and a core tensor via multilinear operations. To exploit the underlying multilinear low-rank structure in high-dimensional datasets, we propose a group-based log-sum penalty functional to place structural sparsity over the core tensor, which leads to a compact representation with smallest core tensor. The method for Tucker decomposition is developed by iteratively minimizing a surrogate function that majorizes the original objective function, which results in an iterative reweighted process. In addition, to reduce the computational complexity, an over-relaxed monotone fast iterative shrinkage-thresholding technique is adapted and embedded in the iterative reweighted process. The proposed method is able to determine the model complexity (i.e. multilinear rank) in an automatic way. Simulation results show that the proposed algorithm offers competitive performance compared with other existing algorithms.
Bin Packing with Multiple Colors
Venue: arXiv cs.DS 2015
Year: 2015

In the Colored Bin Packing problem a set of items with varying weights and colors must be packed into bins of uniform weight limit such that no two items of the same color may be packed adjacently within a bin. We solve this problem for the case where there are two or more colors when the items have zero weight and when the items have unit weight. Our algorithms are optimal and run in linear time. Since our algorithms apply for two or more colors, they demonstrate that the problem does not get harder as the number of colors increases. We also provide closed-form expressions for the optimal number of bins.